On 18/10/06, Travis Oliphant [EMAIL PROTECTED] wrote:
If there are any cases satisfying these rules where a copy does not have
to occur then let me know.
For example, zeros((4,4))[:,1].reshape((2,2)) need not be copied.
I filed a bug in trac and supplied a patch to multiarray.c that avoids
Hi!
I am confused with Numpy behavior with its scalar or 0-d arrays objects:
numpy.__version__
'1.0rc2'
a = numpy.array((1,2,3))
b = a[:2]
b += 1
b
array([2, 3])
a
array([2, 3, 3])
type(b)
type 'numpy.ndarray'
To this point all is ok for me: subarrays share (by default) memory
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A Divendres 20 Octubre 2006 11:42, Sebastien Bardeau va escriure:
[snip]
I can understand that numpy.scalars do not provide inplace operations
(like Python standard scalars, they are immutable), so I'd like to use
0-d Numpy.ndarrays. But:
d = numpy.array(a[2],copy=False)
d += 1
d
Am 20.10.2006 um 02:53 schrieb Jay Parlar:
Hi!
I try to compile numpy rc3 on Panther and get following errors.
(I start build with python2.3 setup.py build to be sure to use the
python shipped with OS X. I din't manage to compile Python2.5 either
yet with similar errors)
Does anynbody has
Hi,
There is an operation I do a lot, I would call it unrolling a array.
The best way to describe it is probably to give the code:
def unroll(M):
Flattens the array M and returns a 2D array with the first columns
being the indices of M, and the last column the flatten M.
On Fri, Oct 20, 2006 at 11:42:26AM +0200, Sebastien Bardeau wrote:
a = numpy.array((1,2,3))
b = a[:2]
Here you index by a slice.
c = a[2]
Whereas here you index by a scalar.
So you want to do
b = a[[2]]
b += 1
or in the general case
b = a[slice(2,3)]
b += 1
Regards
Stéfan
Francesc Altet wrote:
A Divendres 20 Octubre 2006 11:42, Sebastien Bardeau va escriure:
[snip]
I can understand that numpy.scalars do not provide inplace operations
(like Python standard scalars, they are immutable), so I'd like to use
0-d Numpy.ndarrays. But:
d =
Ooops sorry there was two mistakes with the 'hasslice' flag. This seems
now to work for me.
def __getitem__(self,index): # Index may be either an int or a tuple
# Index length:
if type(index) == int: # A single element through first dimension
ilen = 1
index =
Thanks for the comments, Here is the code for the new histogram, tests included. I'll wait for comments or suggestions before submitting a patch (numpy / scipy) ?CheersDavid
2006/10/18, Tim Hochberg [EMAIL PROTECTED]:
My $0.02:If histogram is going to get a makeover, particularly one that makes
Hello.
I have a suggestion that might make slicing using
matrices more user-friendly. I often have a matrix of
row or column numbers that I wish to use as a slice.
If K was a matrix of row numbers (nx1) and M was a nxm
matrix, then I would use ans = M[K.A.ravel(),:] to
obtain the matrix I want.
Hi!
This is probably a silly question but I'm getting confused with a
certain problem: a comparison between experimental data points (2D
points set) and a model (2D points set - no analytical form).
The physical model produces (by a sophisticated simulations done by an
external program) some
Sebastian Żurek wrote:
Hi!
This is probably a silly question but I'm getting confused with a
certain problem: a comparison between experimental data points (2D
points set) and a model (2D points set - no analytical form).
The physical model produces (by a sophisticated simulations done
On 20/10/06, Sebastian Żurek [EMAIL PROTECTED] wrote:
Is there something like that in any numerical python modules (numpy,
pylab) I could use?
In scipy there are some very convenient spline fitting tools which
will allow you to fit a nice smooth spline through the simulation data
points (or
Hi,
i am running numpy on aix compiling with xlc. Revision 1.0rc2 works
fine and passes all tests. But 1.0rc3 and more recent give the
following on import:
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
Warning: invalid value encountered in
Brian Granger wrote:
Hi,
i am running numpy on aix compiling with xlc. Revision 1.0rc2 works
fine and passes all tests. But 1.0rc3 and more recent give the
following on import:
Warning: invalid value encountered in multiply
Warning: invalid value encountered in multiply
Warning: invalid
Also, when I use seterr(all='ignore') the the tests fail:
==
FAIL: Ticket #112
--
Traceback (most recent call last):
File
When I set seterr(all='warn') I see the following:
In [1]: import numpy
/usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/ufunclike.py:46:
RuntimeWarning: invalid value encountered in log
_log2 = umath.log(2)
/usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/scimath.py:19:
Brian Granger wrote:
Also, when I use seterr(all='ignore') the the tests fail:
==
FAIL: Ticket #112
--
Traceback (most recent call last):
File
I have been doing these recent tests with 1.0rc3. I am building from
trunk right now and we will see how that goes. Thanks for your help.
Brian
On 10/20/06, Tim Hochberg [EMAIL PROTECTED] wrote:
Brian Granger wrote:
Also, when I use seterr(all='ignore') the the tests fail:
Brian Granger wrote:
When I set seterr(all='warn') I see the following:
In [1]: import numpy
/usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/ufunclike.py:46:
RuntimeWarning: invalid value encountered in log
_log2 = umath.log(2)
Brian Granger wrote:
Tim,
I just tried everything with r3375. I set seterr(all='warn') and the
tests passed. But all the floating point warning are still there.
With seterr(all='ignore') the warnings go away and all the tests pass.
should I worry about the warnings?
Maybe. I just sent
Thanks, I will investigate more on these things and get back to you
early in the week. But for now numpy seems to be functioning pretty
normally (log(2) gives the correct answer). thanks again.
It would be great to figure this stuff out before 1.0, but we might
not have time.
Brian
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